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In this study, newly identified small molecules were examined for efficacy against ‘Candidatus Liberibacter asiaticus’ in commercial groves of sweet orange (Citrus sinensis) and white grapefruit (Citrus paradisi) trees. We used benzbromarone and/or tolfenamic acid delivered by trunk injection. We evaluated safety and efficacy parameters by performing RNAseq of the citrus host responses, 16S rRNA gene sequencing to characterize citrus-associated microbial communities during treatment, and qRT-PCR as an indirect determination of ‘Ca. L. asiaticus’ viability. Analyses of the C. sinensis transcriptome indicated that each treatment consistently induced genes associated with normal metabolism and growth, without compromising tree viability or negatively affecting the indigenous citrus-associated microbiota. It was found that treatment-associated reduction in ‘Ca. L. asiaticus’ was positively correlated with the proliferation of several core taxa related with citrus health. No symptoms of phytotoxicity were observed in any of the treated trees. Trials were also performed in commercial groves to examine the effect of each treatment on fruit productivity, juice quality and efficacy against ‘Ca. L. asiaticus’. Increased fruit production (15%) was observed in C. paradisi following twelve months of treatment with benzbromarone and tolfenamic acid. These results were positively correlated with decreased ‘Ca. L. asiaticus’ transcriptional activity in root samples.

Queuosine (Q) is a conserved hypermodification of the wobble base of tRNA containing GUN anticodons but the physiological consequences of Q deficiency are poorly understood in bacteria. This work combines transcriptomic, proteomic and physiological studies to characterize a Q-deficient Escherichia coli K12 MG1655 mutant. The absence of Q led to an increased resistance to nickel and cobalt, and to an increased sensitivity to cadmium, compared to the wild-type (WT) strain. Transcriptomic analysis of the WT and Q-deficient strains, grown in the presence and absence of nickel, revealed that the nickel transporter genes (nikABCDE) are downregulated in the Q– mutant, even when nickel is not added. This mutant is therefore primed to resist to high nickel levels. Downstream analysis of the transcriptomic data suggested that the absence of Q triggers an atypical oxidative stress response, confirmed by the detection of slightly elevated reactive oxygen species (ROS) levels in the mutant, increased sensitivity to hydrogen peroxide and paraquat, and a subtle growth phenotype in a strain prone to accumulation of ROS.

Motivation Molecular pathway databases represent cellular processes in a structured and standardized way. These databases support the community-wide utilization of pathway information in biological research and the computational analysis of high-throughput biochemical data. Although pathway databases are critical in genomics research, the fast progress of biomedical sciences prevents databases from staying up-to-date. Moreover, the compartmentalization of cellular reactions into defined pathways reflects arbitrary choices that might not always be aligned with the needs of the researcher. Today, no tool exists that allow the easy creation of user-defined pathway representations. Results Here we present Padhoc, a pipeline for pathway ad hoc reconstruction. Based on a set of user-provided keywords, Padhoc combines natural language processing, database knowledge extraction, orthology search and powerful graph algorithms to create navigable pathways tailored to the user’s needs. We validate Padhoc with a set of well-established Escherichia coli pathways and demonstrate usability to create not-yet-available pathways in model (human) and non-model (sweet orange) organisms. Availability and implementation Padhoc is freely available at https://github.com/ConesaLab/padhoc. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Best performing named entity recognition (NER) methods for biomedical literature are based on hand-crafted features or task-specific rules, which are costly to produce and difficult to generalize to other corpora. End-to-end neural networks achieve state-of-the-art performance without hand-crafted features and task-specific knowledge in non-biomedical NER tasks. However, in the biomedical domain, using the same architecture does not yield competitive performance compared with conventional machine learning models. Results We propose a novel end-to-end deep learning approach for biomedical NER tasks that leverages the local contexts based on n-gram character and word embeddings via Convolutional Neural Network (CNN). We call this approach GRAM-CNN. To automatically label a word, this method uses the local information around a word. Therefore, the GRAM-CNN method does not require any specific knowledge or feature engineering and can be theoretically applied to a wide range of existing NER problems. The GRAM-CNN approach was evaluated on three well-known biomedical datasets containing different BioNER entities. It obtained an F1-score of 87.26% on the Biocreative II dataset, 87.26% on the NCBI dataset and 72.57% on the JNLPBA dataset. Those results put GRAM-CNN in the lead of the biological NER methods. To the best of our knowledge, we are the first to apply CNN based structures to BioNER problems. Availability and implementation The GRAM-CNN source code, datasets and pre-trained model are available online at: https://github.com/valdersoul/GRAM-CNN. Supplementary information Supplementary data are available at Bioinformatics online.

Gene families evolve by the processes of speciation (creating orthologs), gene duplication (paralogs), and horizontal gene transfer (xenologs), in addition to sequence divergence and gene loss. Orthologs in particular play an essential role in comparative genomics and phylogenomic analyses. With the continued sequencing of organisms across the tree of life, the data are available to reconstruct the unique evolutionary histories of tens of thousands of gene families. Accurate reconstruction of these histories, however, is a challenging computational problem, and the focus of the Quest for Orthologs Consortium. We review the recent advances and outstanding challenges in this field, as revealed at a symposium and meeting held at the University of Southern California in 2017. Key advances have been made both at the level of orthology algorithm development and with respect to coordination across the community of algorithm developers and orthology end-users. Applications spanned a broad range, including gene function prediction, phylostratigraphy, genome evolution, and phylogenomics. The meetings highlighted the increasing use of meta-analyses integrating results from multiple different algorithms, and discussed ongoing challenges in orthology inference as well as the next steps toward improvement and integration of orthology resources. We thank the Society for Molecular Biology and Evolution for supporting the SMBE-EGF Symposium, and the University of Southern California for supporting the QFO5 meeting. We wish to acknowledge the following support for attending the meetings and/or writing this manuscript: National Institutes of Health (grant HG002273 for P.D.T.; HG007822 for M.J.M. and A.S.d.S.; GM122480, HD085901, DK110520 for E.M.M.; GM123683 for C.M.), Wellcome Trust (grant WT108749/Z/15/Z for M.M. and M.P.), Swiss National Science Foundation (grant 150654 for C.D.), European Commission (contract 676559 for S.C.G.), Welch Foundation (grant F-1515 for E.M.M.), European Molecular Biology Laboratory (for M.J.M., M.M., M.P.), and National Bioscience Database Center and Japan Science and Technology Agency (for I.U.).

This paper introduces STRASS: Summarization by TRAnsformation Selection and Scoring. It is an extractive text summarization method which leverages the semantic information in existing sentence embedding spaces. Our method creates an extractive summary by selecting the sentences with the closest embeddings to the document embedding. The model learns a transformation of the document embedding to minimize the similarity between the extractive summary and the ground truth summary. As the transformation is only composed of a dense layer, the training can be done on CPU, therefore, inexpensive. Moreover, inference time is short and linear according to the number of sentences. As a second contribution, we introduce the French CASS dataset, composed of judgments from the French Court of cassation and their corresponding summaries. On this dataset, our results show that our method performs similarly to the state of the art extractive methods with effective training and inferring time., Comment: To appear in 2019 ACL Student Research Workshop

18 Pages, 5 Figures. Sequencing data from this study have been submitted to the NCBI Sequence Read Archive (SRA; https://www.ncbi.nlm.nih.gov/sra) under study accession number SRP101446., High-throughput sequencing of full-length transcripts using long reads has paved the way for the discovery of thousands of novel transcripts, even in well-annotated mammalian species. The advances in sequencing technology have created a need for studies and tools that can characterize these novel variants. Here, we present SQANTI, an automated pipeline for the classification of long-read transcripts that can assess the quality of data and the preprocessing pipeline using 47 unique descriptors. We apply SQANTI to a neuronal mouse transcriptome using Pacific Biosciences (PacBio) long reads and illustrate how the tool is effective in characterizing and describing the composition of the full-length transcriptome. We perform extensive evaluation of ToFU PacBio transcripts by PCR to reveal that an important number of the novel transcripts are technical artifacts of the sequencing approach and that SQANTI quality descriptors can be used to engineer a filtering strategy to remove them. Most novel transcripts in this curated transcriptome are novel combinations of existing splice sites, resulting more frequently in novel ORFs than novel UTRs, and are enriched in both general metabolic and neural-specific functions. We show that these new transcripts have a major impact in the correct quantification of transcript levels by state-of-the-art short-read-based quantification algorithms. By comparing our iso-transcriptome with public proteomics databases, we find that alternative isoforms are elusive to proteogenomics detection. SQANTI allows the user to maximize the analytical outcome of long-read technologies by providing the tools to deliver quality-evaluated and curated full-length transcriptomes., This work has been partially funded by the University of Florida Preeminence hires program, the Spanish Ministry of Economy and Competitiveness grants BIO2015- 71658-R, BFU2014-57636-P, Spanish Ministry of Education grant FPU2013/02348, and GENCODE NIH grant 2U41 HG007234. This project has received funding from the Marie Curie International Research Staff Exchange Scheme within the 7th European Community Framework Program under grant agreement No 612583-DEANN.

High-throughput sequencing of full-length transcripts using long reads has paved the way for the discovery of thousands of novel transcripts, even in very well annotated organisms as mice and humans. Nonetheless, there is a need for studies and tools that characterize these novel isoforms. Here we present SQANTI, an automated pipeline for the classification of long-read transcripts that computes 47 descriptors that can be used to assess the quality of the data and of the preprocessing pipelines. We applied SQANTI to a neuronal mouse transcriptome using PacBio long reads and illustrate how the tool is effective in readily describing the composition of and characterizing the full-length transcriptome. We perform extensive evaluation of ToFU PacBio transcripts by PCR to reveal that an important number of the novel transcripts are technical artifacts of the sequencing approach, and that SQANTI quality descriptors can be used to engineer a filtering strategy to remove them. Most novel transcripts in this curated transcriptome are novel combinations of existing splice sites, result more frequently in novel ORFs than novel UTRs and are enriched in both general metabolic and neural specific functions. We show that these new transcripts have a major impact in the correct quantification of transcript levels by state-of-the-art short-read based quantification algorithms. By comparing our iso-transcriptome with public proteomics databases we find that alternative isoforms are elusive to proteogenomics detection and are variable in protein changes with respect to the principal isoform of their genes. SQANTI allows the user to maximize the analytical outcome of long read technologies by providing the tools to deliver quality-evaluated and curated full-length transcriptomes. SQANTI is available at https://bitbucket.org/ConesaLab/sqanti.

Achieving high accuracy in orthology inference is essential for many comparative, evolutionary and functional genomic analyses, yet the true evolutionary history of genes is generally unknown and orthologs are used for very different applications across phyla, requiring different precision–recall trade-offs. As a result, it is difficult to assess the performance of orthology inference methods. Here, we present a community effort to establish standards and an automated web-based service to facilitate orthology benchmarking. Using this service, we characterize 15 well-established inference methods and resources on a battery of 20 different benchmarks. Standardized benchmarking provides a way for users to identify the most effective methods for the problem at hand, sets a minimum requirement for new tools and resources, and guides the development of more accurate orthology inference methods., Nature Methods, 13 (5), ISSN:1548-7105, ISSN:1548-7091

BACKGROUND: During acute ventilatory failure in patients with chronic obstructive pulmonary disease (COPD), applying external positive end-expiratory pressure (PEEPe) will reopen small airways and, thus, may enhance peripheral deposition as well as the physiological effects of inhaled beta-2 agonists.OBJECTIVE: To investigate the efficacy of inhaled fenoterol applied by zero end-expiratory pressure (ZEEPe) or PEEPe.METHODS: Ten patients with COPD who were intubated and mechanically ventilated received fenoterol (10 mg/4 mL) via the ventilator using a jet nebulizer for 30 min on ZEEPe and PEEPe set at 80% of the total PEEP in a random order. The total resistance of the respiratory system (rapid airway occlusion technique), change in end-expiratory lung volume and expiratory flow limitation were assessed before and 5 min, 15 min, 30 min, 60 min and 240 min after fenoterol inhalation.RESULTS: Before inhalation and 60 min after inhalation, the total PEEP, the change in end-expiratory lung volume and the total resistance of the respiratory system were 8±3 cmH2O and 6±3 cmH2O, 0.61±0.34 L and 0.43±0.32 L, and 26±7 cmH2O/L/s and 23±6 cmH2O/L/s, respectively, with ZEEPe, and 9±3 cmH2O and 8±3 cmH2O (P2O/L/s and 25±9 H2O/L/s, respectively, with PEEPe. Three patients became not flow-limited under the combination of PEEPe and fenoterol.CONCLUSIONS: In patients with COPD, fenoterol combined with PEEPe has opposing effects on respiratory mechanics. First, it does not significantly reduce lung hyperinflation or inspiratory resistances. Second, it allows expiratory flow limitation reversal in some patients. These findings result from the net effect on end-expiratory lung volume of each intervention. This implies that if fenoterol is used in combination with PEEPe, the level of PEEPe should be reassessed during the time course of the drug to prevent any further lung hyperinflation.

To assess incidence and magnitude of the “lower inflection point” of the chest wall, the sigmoidal equation was used in 36 consecutive patients intubated and mechanically ventilated with acute lung injury (ALI). They were 21 primary and 5 secondary ALI, 6 unilateral pneumonia, and 4 cardiogenic pulmonary edema. The lower inflection point was estimated as the point of maximal compliance increase. The low constant flow inflation method and esophageal pressure were used to partition the volume-pressure curves into their chest wall and lung components on zero end-expiratory pressure. The sigmoidal equation had an excellent fit with coefficients of determination >0.90 in all instances. The point of maximal compliance increase of the chest wall ranged from 0 to 8.3 cmH2O (median 1 cmH2O) with no difference between ALI groups. The chest wall significantly contributed to the lower inflection point of the respiratory system in eight patients only. The occurrence of a significant contribution of the chest wall to the lower inflection point of the respiratory system is lower than anticipated. The sigmoidal equation is able to determine precisely the point of the maximal compliance increase of lung and chest wall.

International audience; Background: In comparative genomics, orthologs are used to transfer annotation from genes already characterized to newly sequenced genomes. Many methods have been developed for finding orthologs in sets of genomes. However, the application of different methods on the same proteome set can lead to distinct orthology predictions. Methods: We developed a method based on a meta-approach that is able to combine the results of several methods for orthologous group prediction. The purpose of this method is to produce better quality results by using the overlapping results obtained from several individual orthologous gene prediction procedures. Our method proceeds in two steps. The first aims to construct seeds for groups of orthologous genes; these seeds correspond to the exact overlaps between the results of all or several methods. In the second step, these seed groups are expanded by using HMM profiles. Results: We evaluated our method on two standard reference benchmarks, OrthoBench and Orthology Benchmark Service. Our method presents a higher level of accurately predicted groups than the individual input methods of orthologous group prediction. Moreover, our method increases the number of annotated orthologous pairs without decreasing the annotation quality compared to twelve state-of-the-art methods. Conclusions: The meta-approach based method appears to be a reliable procedure for predicting orthologous groups. Since a large number of methods for predicting groups of orthologous genes exist, it is quite conceivable to apply this meta-approach to several combinations of different methods.

A 28-year-old male patient, treated with prednisone for bronchitis with sibilant rales, developed fever with abdominal pain and generalized vesicular rash after coming in contact with varicella-infected children. He was hospitalized after having a seizure. Laboratory values revealed hepatitis and rapidly fulminant hepatic insufficiency with disseminated intravascular coagulation. Despite acyclovir treatment, the patient died 4 days after admission. Clinical presentation could evoke a Reye's syndrome, but liver biopsy showed massive coagulative necrosis. This report demonstrates the increased risk of complicated varicella associated with the use of corticosteroids, even for a short period of time.